"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from copy import deepcopy

import torch
import torch.nn as nn
import torch.nn.functional as F

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg, overlay_external_default_cfg
from .layers import to_2tuple, trunc_normal_, DropPath, PatchEmbed, LayerNorm2d, create_classifier
from .registry import register_model


__all__ = ['Visformer']


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
        'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'stem.0', 'classifier': 'head',
        **kwargs
    }


default_cfgs = dict(
    visformer_tiny=_cfg(),
    visformer_small=_cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/visformer_small-839e1f5b.pth'
    ),
)


class SpatialMlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None,
                 act_layer=nn.GELU, drop=0., group=8, spatial_conv=False):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.in_features = in_features
        self.out_features = out_features
        self.spatial_conv = spatial_conv
        if self.spatial_conv:
            if group < 2:  # net setting
                hidden_features = in_features * 5 // 6
            else:
                hidden_features = in_features * 2
        self.hidden_features = hidden_features
        self.group = group
        self.drop = nn.Dropout(drop)
        self.conv1 = nn.Conv2d(in_features, hidden_features, 1, stride=1, padding=0, bias=False)
        self.act1 = act_layer()
        if self.spatial_conv:
            self.conv2 = nn.Conv2d(
                hidden_features, hidden_features, 3, stride=1, padding=1, groups=self.group, bias=False)
            self.act2 = act_layer()
        else:
            self.conv2 = None
            self.act2 = None
        self.conv3 = nn.Conv2d(hidden_features, out_features, 1, stride=1, padding=0, bias=False)

    def forward(self, x):
        x = self.conv1(x)
        x = self.act1(x)
        x = self.drop(x)
        if self.conv2 is not None:
            x = self.conv2(x)
            x = self.act2(x)
        x = self.conv3(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, head_dim_ratio=1., attn_drop=0., proj_drop=0.):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        head_dim = round(dim // num_heads * head_dim_ratio)
        self.head_dim = head_dim
        self.scale = head_dim ** -0.5
        self.qkv = nn.Conv2d(dim, head_dim * num_heads * 3, 1, stride=1, padding=0, bias=False)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Conv2d(self.head_dim * self.num_heads, dim, 1, stride=1, padding=0, bias=False)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, C, H, W = x.shape
        x = self.qkv(x).reshape(B, 3, self.num_heads, self.head_dim, -1).permute(1, 0, 2, 4, 3)
        q, k, v = x[0], x[1], x[2]

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        x = attn @ v

        x = x.permute(0, 1, 3, 2).reshape(B, -1, H, W)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Block(nn.Module):
    def __init__(self, dim, num_heads, head_dim_ratio=1., mlp_ratio=4.,
                 drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=LayerNorm2d,
                 group=8, attn_disabled=False, spatial_conv=False):
        super().__init__()
        self.spatial_conv = spatial_conv
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        if attn_disabled:
            self.norm1 = None
            self.attn = None
        else:
            self.norm1 = norm_layer(dim)
            self.attn = Attention(
                dim, num_heads=num_heads, head_dim_ratio=head_dim_ratio, attn_drop=attn_drop, proj_drop=drop)

        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = SpatialMlp(
            in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop,
            group=group, spatial_conv=spatial_conv)  # new setting

    def forward(self, x):
        if self.attn is not None:
            x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class Visformer(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, init_channels=32, embed_dim=384,
                 depth=12, num_heads=6, mlp_ratio=4., drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
                 norm_layer=LayerNorm2d, attn_stage='111', pos_embed=True, spatial_conv='111',
                 vit_stem=False, group=8, global_pool='avg', conv_init=False, embed_norm=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        self.num_classes = num_classes
        self.embed_dim = embed_dim
        self.init_channels = init_channels
        self.img_size = img_size
        self.vit_stem = vit_stem
        self.conv_init = conv_init
        if isinstance(depth, (list, tuple)):
            self.stage_num1, self.stage_num2, self.stage_num3 = depth
            depth = sum(depth)
        else:
            self.stage_num1 = self.stage_num3 = depth // 3
            self.stage_num2 = depth - self.stage_num1 - self.stage_num3
        self.pos_embed = pos_embed
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]

        # stage 1
        if self.vit_stem:
            self.stem = None
            self.patch_embed1 = PatchEmbed(
                img_size=img_size, patch_size=patch_size, in_chans=in_chans,
                embed_dim=embed_dim, norm_layer=embed_norm, flatten=False)
            img_size = [x // 16 for x in img_size]
        else:
            if self.init_channels is None:
                self.stem = None
                self.patch_embed1 = PatchEmbed(
                    img_size=img_size, patch_size=patch_size // 2, in_chans=in_chans,
                    embed_dim=embed_dim // 2, norm_layer=embed_norm, flatten=False)
                img_size = [x // 8 for x in img_size]
            else:
                self.stem = nn.Sequential(
                    nn.Conv2d(in_chans, self.init_channels, 7, stride=2, padding=3, bias=False),
                    nn.BatchNorm2d(self.init_channels),
                    nn.ReLU(inplace=True)
                )
                img_size = [x // 2 for x in img_size]
                self.patch_embed1 = PatchEmbed(
                    img_size=img_size, patch_size=patch_size // 4, in_chans=self.init_channels,
                    embed_dim=embed_dim // 2, norm_layer=embed_norm, flatten=False)
                img_size = [x // 4 for x in img_size]

        if self.pos_embed:
            if self.vit_stem:
                self.pos_embed1 = nn.Parameter(torch.zeros(1, embed_dim, *img_size))
            else:
                self.pos_embed1 = nn.Parameter(torch.zeros(1, embed_dim//2, *img_size))
            self.pos_drop = nn.Dropout(p=drop_rate)
        self.stage1 = nn.ModuleList([
            Block(
                dim=embed_dim//2, num_heads=num_heads, head_dim_ratio=0.5, mlp_ratio=mlp_ratio,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                group=group, attn_disabled=(attn_stage[0] == '0'), spatial_conv=(spatial_conv[0] == '1')
            )
            for i in range(self.stage_num1)
        ])

        # stage2
        if not self.vit_stem:
            self.patch_embed2 = PatchEmbed(
                img_size=img_size, patch_size=patch_size // 8, in_chans=embed_dim // 2,
                embed_dim=embed_dim, norm_layer=embed_norm, flatten=False)
            img_size = [x // 2 for x in img_size]
            if self.pos_embed:
                self.pos_embed2 = nn.Parameter(torch.zeros(1, embed_dim, *img_size))
        self.stage2 = nn.ModuleList([
            Block(
                dim=embed_dim, num_heads=num_heads, head_dim_ratio=1.0, mlp_ratio=mlp_ratio,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                group=group, attn_disabled=(attn_stage[1] == '0'), spatial_conv=(spatial_conv[1] == '1')
            )
            for i in range(self.stage_num1, self.stage_num1+self.stage_num2)
        ])

        # stage 3
        if not self.vit_stem:
            self.patch_embed3 = PatchEmbed(
                img_size=img_size, patch_size=patch_size // 8, in_chans=embed_dim,
                embed_dim=embed_dim * 2, norm_layer=embed_norm, flatten=False)
            img_size = [x // 2 for x in img_size]
            if self.pos_embed:
                self.pos_embed3 = nn.Parameter(torch.zeros(1, embed_dim*2, *img_size))
        self.stage3 = nn.ModuleList([
            Block(
                dim=embed_dim*2, num_heads=num_heads, head_dim_ratio=1.0, mlp_ratio=mlp_ratio,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                group=group, attn_disabled=(attn_stage[2] == '0'), spatial_conv=(spatial_conv[2] == '1')
            )
            for i in range(self.stage_num1+self.stage_num2, depth)
        ])

        # head
        self.num_features = embed_dim if self.vit_stem else embed_dim * 2
        self.norm = norm_layer(self.num_features)
        self.global_pool, self.head = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)

        # weights init
        if self.pos_embed:
            trunc_normal_(self.pos_embed1, std=0.02)
            if not self.vit_stem:
                trunc_normal_(self.pos_embed2, std=0.02)
                trunc_normal_(self.pos_embed3, std=0.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.BatchNorm2d):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            if self.conv_init:
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            else:
                trunc_normal_(m.weight, std=0.02)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0.)

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool='avg'):
        self.num_classes = num_classes
        self.global_pool, self.head = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)

    def forward_features(self, x):
        if self.stem is not None:
            x = self.stem(x)

        # stage 1
        x = self.patch_embed1(x)
        if self.pos_embed:
            x = x + self.pos_embed1
            x = self.pos_drop(x)
        for b in self.stage1:
            x = b(x)

        # stage 2
        if not self.vit_stem:
            x = self.patch_embed2(x)
            if self.pos_embed:
                x = x + self.pos_embed2
                x = self.pos_drop(x)
        for b in self.stage2:
            x = b(x)

        # stage3
        if not self.vit_stem:
            x = self.patch_embed3(x)
            if self.pos_embed:
                x = x + self.pos_embed3
                x = self.pos_drop(x)
        for b in self.stage3:
            x = b(x)

        x = self.norm(x)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.global_pool(x)
        x = self.head(x)
        return x


def _create_visformer(variant, pretrained=False, default_cfg=None, **kwargs):
    if kwargs.get('features_only', None):
        raise RuntimeError('features_only not implemented for Vision Transformer models.')
    model = build_model_with_cfg(
        Visformer, variant, pretrained,
        default_cfg=default_cfgs[variant],
        **kwargs)
    return model


@register_model
def visformer_tiny(pretrained=False, **kwargs):
    model_cfg = dict(
        init_channels=16, embed_dim=192, depth=(7, 4, 4), num_heads=3, mlp_ratio=4., group=8,
        attn_stage='011', spatial_conv='100', norm_layer=nn.BatchNorm2d, conv_init=True,
        embed_norm=nn.BatchNorm2d, **kwargs)
    model = _create_visformer('visformer_tiny', pretrained=pretrained, **model_cfg)
    return model


@register_model
def visformer_small(pretrained=False, **kwargs):
    model_cfg = dict(
        init_channels=32, embed_dim=384, depth=(7, 4, 4), num_heads=6, mlp_ratio=4., group=8,
        attn_stage='011', spatial_conv='100', norm_layer=nn.BatchNorm2d, conv_init=True,
        embed_norm=nn.BatchNorm2d, **kwargs)
    model = _create_visformer('visformer_small', pretrained=pretrained, **model_cfg)
    return model


# @register_model
# def visformer_net1(pretrained=False, **kwargs):
#     model = Visformer(
#         init_channels=None, embed_dim=384, depth=(0, 12, 0), num_heads=6, mlp_ratio=4., attn_stage='111',
#         spatial_conv='000', vit_stem=True, conv_init=True, **kwargs)
#     model.default_cfg = _cfg()
#     return model
#
#
# @register_model
# def visformer_net2(pretrained=False, **kwargs):
#     model = Visformer(
#         init_channels=32, embed_dim=384, depth=(0, 12, 0), num_heads=6, mlp_ratio=4., attn_stage='111',
#         spatial_conv='000', vit_stem=False, conv_init=True, **kwargs)
#     model.default_cfg = _cfg()
#     return model
#
#
# @register_model
# def visformer_net3(pretrained=False, **kwargs):
#     model = Visformer(
#         init_channels=32, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4., attn_stage='111',
#         spatial_conv='000', vit_stem=False, conv_init=True, **kwargs)
#     model.default_cfg = _cfg()
#     return model
#
#
# @register_model
# def visformer_net4(pretrained=False, **kwargs):
#     model = Visformer(
#         init_channels=32, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4., attn_stage='111',
#         spatial_conv='000', vit_stem=False, conv_init=True, **kwargs)
#     model.default_cfg = _cfg()
#     return model
#
#
# @register_model
# def visformer_net5(pretrained=False, **kwargs):
#     model = Visformer(
#         init_channels=32, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4., group=1, attn_stage='111',
#         spatial_conv='111', vit_stem=False, conv_init=True, **kwargs)
#     model.default_cfg = _cfg()
#     return model
#
#
# @register_model
# def visformer_net6(pretrained=False, **kwargs):
#     model = Visformer(
#         init_channels=32, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4., group=1, attn_stage='111',
#         pos_embed=False, spatial_conv='111', conv_init=True, **kwargs)
#     model.default_cfg = _cfg()
#     return model
#
#
# @register_model
# def visformer_net7(pretrained=False, **kwargs):
#     model = Visformer(
#         init_channels=32, embed_dim=384, depth=(6, 7, 7), num_heads=6, group=1, attn_stage='000',
#         pos_embed=False, spatial_conv='111', conv_init=True, **kwargs)
#     model.default_cfg = _cfg()
#     return model




